Maintaining (Locus of) Control? Data Combination for the Identification and Inference of Factor Structure Models

Research output: Contribution to journal › Journal article › Research › peer-review

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Maintaining (Locus of) Control? Data Combination for the Identification and Inference of Factor Structure Models. / Piatek, Rémi; Pinger, Pia.

In: Journal of Applied Econometrics, Vol. 31, No. 4, 06.2016, p. 734-755.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Piatek, R & Pinger, P 2016, 'Maintaining (Locus of) Control? Data Combination for the Identification and Inference of Factor Structure Models', Journal of Applied Econometrics, vol. 31, no. 4, pp. 734-755. https://doi.org/10.1002/jae.2456

APA

Piatek, R., & Pinger, P. (2016). Maintaining (Locus of) Control? Data Combination for the Identification and Inference of Factor Structure Models. Journal of Applied Econometrics, 31(4), 734-755. https://doi.org/10.1002/jae.2456

Vancouver

Piatek R, Pinger P. Maintaining (Locus of) Control? Data Combination for the Identification and Inference of Factor Structure Models. Journal of Applied Econometrics. 2016 Jun;31(4):734-755. https://doi.org/10.1002/jae.2456

Author

Piatek, Rémi ; Pinger, Pia. / Maintaining (Locus of) Control? Data Combination for the Identification and Inference of Factor Structure Models. In: Journal of Applied Econometrics. 2016 ; Vol. 31, No. 4. pp. 734-755.

Bibtex

@article{0fac6d5151c646f580a99853d225c94d,
title = "Maintaining (Locus of) Control?: Data Combination for the Identification and Inference of Factor Structure Models",
abstract = "Factor structure models are widely used in economics to extract latent variables, such as personality traits, and to measure their impact on outcomes of interest. The identification and inference of these models, however, highly depend on the availability of rich longitudinal data. To overcome the common problem of data scarcity, this paper proposes to combine data sets that each identify some part of the likelihood, thereby recovering the identification of the complete model. The performance of the approach is demonstrated by a Monte Carlo experiment. We apply this technique empirically to study the impact of locus of control on education and wages. Our strategy allows us to elicit the distribution of pre-market locus of control from a sample of young individuals, and to measure its impact on education and wages in a sample of adults. Our findings indicate that the effect of locus of control on wages mainly operates through education.",
keywords = "Faculty of Social Sciences, Locus of Control, Factor Structure Model, Dataset Combination, Bayesian Factor Analysis, Wages, Education",
author = "R{\'e}mi Piatek and Pia Pinger",
note = "JEL Classification: C31, J24, J31",
year = "2016",
month = jun,
doi = "10.1002/jae.2456",
language = "English",
volume = "31",
pages = "734--755",
journal = "Journal of Applied Econometrics",
issn = "0883-7252",
publisher = "JohnWiley & Sons Ltd",
number = "4",

}

RIS

TY - JOUR

T1 - Maintaining (Locus of) Control?

T2 - Data Combination for the Identification and Inference of Factor Structure Models

AU - Piatek, Rémi

AU - Pinger, Pia

N1 - JEL Classification: C31, J24, J31

PY - 2016/6

Y1 - 2016/6

N2 - Factor structure models are widely used in economics to extract latent variables, such as personality traits, and to measure their impact on outcomes of interest. The identification and inference of these models, however, highly depend on the availability of rich longitudinal data. To overcome the common problem of data scarcity, this paper proposes to combine data sets that each identify some part of the likelihood, thereby recovering the identification of the complete model. The performance of the approach is demonstrated by a Monte Carlo experiment. We apply this technique empirically to study the impact of locus of control on education and wages. Our strategy allows us to elicit the distribution of pre-market locus of control from a sample of young individuals, and to measure its impact on education and wages in a sample of adults. Our findings indicate that the effect of locus of control on wages mainly operates through education.

AB - Factor structure models are widely used in economics to extract latent variables, such as personality traits, and to measure their impact on outcomes of interest. The identification and inference of these models, however, highly depend on the availability of rich longitudinal data. To overcome the common problem of data scarcity, this paper proposes to combine data sets that each identify some part of the likelihood, thereby recovering the identification of the complete model. The performance of the approach is demonstrated by a Monte Carlo experiment. We apply this technique empirically to study the impact of locus of control on education and wages. Our strategy allows us to elicit the distribution of pre-market locus of control from a sample of young individuals, and to measure its impact on education and wages in a sample of adults. Our findings indicate that the effect of locus of control on wages mainly operates through education.

KW - Faculty of Social Sciences

KW - Locus of Control

KW - Factor Structure Model

KW - Dataset Combination

KW - Bayesian Factor Analysis

KW - Wages

KW - Education

U2 - 10.1002/jae.2456

DO - 10.1002/jae.2456

M3 - Journal article

VL - 31

SP - 734

EP - 755

JO - Journal of Applied Econometrics

JF - Journal of Applied Econometrics

SN - 0883-7252

IS - 4

ER -

ID: 130767958